Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization and Monte Carlo methods for solving hybrid Bayesian networks. Any probability density function (PDF) can be approximated by an MTE potential, which can always be marginalized in closed form. This allows propagation to be done exactly using the Shenoy-Shafer architecture for computing marginals, with no restrictions on the construction of a join tree. This paper presents MTE potentials that approximate standard PDF’s and applications of these potentials for solving inference problems in hybrid Bayesian networks. These approximations will extend the types of inference problems that can be modelled with Bayesian networks, as demonstrated using three exampl...
Mixtures of truncated exponentials (MTEs) are a powerful alternative to discretisation when working ...
AbstractMixtures of truncated exponentials (MTEs) are a powerful alternative to discretisation when ...
Bayesian networks with mixtures of truncated exponentials (MTEs) support efficient inference algorit...
Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization and Monte C...
AbstractMixtures of truncated exponentials (MTE) potentials are an alternative to discretization for...
Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization and Monte C...
Has been accepted for publication in the International Journal of Approximate Reasoning, Elsevier Sc...
Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for approxi...
Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for solving...
The MTE (Mixture of Truncated Exponentials) model allows to deal with Bayesian networks containing d...
AbstractThe main goal of this paper is to describe inference in hybrid Bayesian networks (BNs) using...
In this paper we introduce an algorithm for learning hybrid Bayesian networks from data. The result ...
AbstractIn this paper we introduce an algorithm for learning hybrid Bayesian networks from data. The...
In this paper we propose a framework, called mixtures of truncated basis functions (MoTBFs), for rep...
The main goal of this paper is to describe a method for exact inference in general hybrid Bayesian n...
Mixtures of truncated exponentials (MTEs) are a powerful alternative to discretisation when working ...
AbstractMixtures of truncated exponentials (MTEs) are a powerful alternative to discretisation when ...
Bayesian networks with mixtures of truncated exponentials (MTEs) support efficient inference algorit...
Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization and Monte C...
AbstractMixtures of truncated exponentials (MTE) potentials are an alternative to discretization for...
Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization and Monte C...
Has been accepted for publication in the International Journal of Approximate Reasoning, Elsevier Sc...
Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for approxi...
Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for solving...
The MTE (Mixture of Truncated Exponentials) model allows to deal with Bayesian networks containing d...
AbstractThe main goal of this paper is to describe inference in hybrid Bayesian networks (BNs) using...
In this paper we introduce an algorithm for learning hybrid Bayesian networks from data. The result ...
AbstractIn this paper we introduce an algorithm for learning hybrid Bayesian networks from data. The...
In this paper we propose a framework, called mixtures of truncated basis functions (MoTBFs), for rep...
The main goal of this paper is to describe a method for exact inference in general hybrid Bayesian n...
Mixtures of truncated exponentials (MTEs) are a powerful alternative to discretisation when working ...
AbstractMixtures of truncated exponentials (MTEs) are a powerful alternative to discretisation when ...
Bayesian networks with mixtures of truncated exponentials (MTEs) support efficient inference algorit...